 Open Access
 Total Downloads : 437
 Authors : Prof. Pooja Agarwal, Ritesh Diwan
 Paper ID : IJERTV2IS2379
 Volume & Issue : Volume 02, Issue 02 (February 2013)
 Published (First Online): 28022013
 ISSN (Online) : 22780181
 Publisher Name : IJERT
 License: This work is licensed under a Creative Commons Attribution 4.0 International License
Sensorless Controlling of the Permanent Magnet Synchronous Motor using Space vector Control with Measurement Vector Insertion
Prof. Pooja Agarwal Prof. Ritesh Diwan
Abstract: The surfacemounted permanent magnet synchronous motors (SPMSM) are the first preference of the designers for the industrial applications because of its high efficiency and better torque to current ratio. Now as for as the controlling is concern the measurement of speed and rotor positions are generally performed by sensors but the involvement of the sensors increases the cost and reduces the roughness of the motor it also increases the maintenance. Because of these reasons the sensorless controlling of the motor is a field of intensive and demanding research. In this paper we are presenting the space vector based controlling of the SPMSM which involves the measurement vectors [1] and the presented controlling system. The measurement vector technique reduces the distortion of the measured current hence improves the efficiency of the controlling system.
Keywords: SurfaceMounted Permanent Magnet Synchronous Motors (SPMSM), Sensorless Controlling, Measurement Vector.

Introduction
The permanent magnet synchronous motors (PMSM) are gaining applications in many fields such as traction, automobiles, robotics and aerospace technology. The major advantage of using the PMSM over induction motors is that the power density of permanent magnet synchronous motor is higher than one of
induction motor with the same ratings due to the no stator power dedicated to the magnetic field production [2]. Other advantage involves the higher efficiency and greater torque to current ratio. Like other motors the PMSM is also required a perfect control mechanism or system. The control of PM synchronous motors requires the knowledge of the rotor magnet axis position (briefly rotor position). For this reason they are equipped with some kind of transducer, such as encoders or resolvers, able to provide that information. This additional component involves cost, encumbrance, wiring, alignment procedures, and others tedious disadvantages [3]. Therefore researchers are working for eliminating these sensors or transducers by estimating the position from current and voltages of the motor terminals. The rest of the paper is arranged as that section 2 provides a useful literature descriptions related to same field followed by the space vector controlling and measurement vector explanations in section 3 and 4 respectively. In section 5 the structure of the proposed system is described then the simulated results and conclusion is presented in section 6 and 7 respectively.

Related Work
Because of the requirement of such system many researchers has shown their interest and presented their works in different literatures some of them studies during the development of this paper is presented in this section. K. Boughrara, D. Zarko, R. Ibtiouen, O. Touhami,
and A. Rezzoug [4] presented original numerical SchwarzChristoffel (SC) transformation to analyze magnetic field originating from permanent magnets and the armature winding currents in a slotted air gap of an inset permanentmagnet synchronous motor. The vector control of PMSM for practical application is described by Milan Brejl & Michal Princ [5], the literature describes the working of PMSM in detail with practical considerations it also presented the real time development circuits with microprocessor. Al Kassem Jebai, Francois Malrait, Philippe Martin and Pierre Rouchon [6] proposed Sensorless position estimation of PermanentMagnet Synchronous Motors using a saturation model. Their work proposes a clear and original analysis based on second order averaging of how to recover the position information from signal injection; this analysis blends well with a general model of magnetic saturation. They also proposes a simple parametric model of the saturated PMSM, based on an energy function which simply encompasses saturation and crosssaturation effects. The Back EMF Observers base speed and position estimation method is proposed by Marco Tursini, Roberto Petrella and Alessia Scafati [7] their work deals with the self compensation of the intrinsic estimation error in backEMF based rotor position observers for PM synchronous motors. The self compensation is based on the analytical calculation of the rotor position estimation error for two types of popular backEMF observers, such as the standardlinear Luenberger Observer and the nonlinear Sliding Mode Observer. Parameters influence on the synchronization process of a PMSM [8] is presented by J. Rais and M. P. DonsiÃ³n this literature presents the modeling and simulation details with specific parameters.

Space Vectors
During normal state, there are eight switching states of inverter which can be expressed as space voltage vector (SA , B and C ) such as (0,0,0), (0,0,1), (0,1,0), (0,1,1), (1,0,0),
(1, 0,1), (1,1,0) and (1,1,1). SA = 1 means upper switch of leg A is on while the lower one is off, and vice versa. The same logic is applicable to SB and C also. Amongst above eight voltage vectors, (0, 0, 0) and (1, 1, 1) are termed as zero vectors while the other six as active vectors [5]. The switching vectors describe the inverter output voltages as shown in Figure 2.
Figure 1: Voltage source inverterinduction motor drive.
Figure 3: Voltage vectors and space sectors [5].

Measurement Vector Insertion Method (MVIM)
A new single current sensing algorithm is proposed in [1] for achieving highquality phase
current reconstruction and regulation using a dc link current sensor. The proposed method effectively overcomes the problem created by the unmeasurable intervals figure 3.
Figure 3: Unmeasurable areas (shaded) in the inverter output voltage space vector plane along sector boundaries.
The new concept introduces a special switching sequence whenever the reference voltage vector falls into one of the unmeasurable regions to insure that all three phase currents are measurable. In the first switching interval
, the PWM algorithm generates a reference voltage vector according to basic SVPWM operation. During the second switching interval the new method introduces three special measurement vectors as shown in Fig. 4 and 5, so that all three phase currents can be sequentially measured during this interval.
Figure 4: Basic concept of MVIM.
Figure 5: Example of PWM timing waveforms for basic MVIM algorithm for reference vector in vicinity of (100) active voltage vector with Ts1
= Ts2.
A group of three active space vectors consisting of [100], [010], and [001] can be utilized for the measurement vectors, or, alternatively, a second group of active space vectors [110], [011], and [101] can be applied. This new algorithm will be referred to as the measurement vector insertion method (MVIM).

Proposed Method
The proposed system is divided in two parts in first part we estimates the phase currents using single current sensor at the dc link only and by using the space vector method with measurement vectors insertion. And then the
equation [1] is used to estimate the rotor position and speed
In this method, the flux linkage is estimated from measured voltages and currents and then the position is predicted by use of polynomial curve fitting [9]. The fundamental idea is to take the voltage equation of the machine,
Where, V is the input voltage, i is the current, R is the resistance, and is the flux linkage, respectively. Based on the initial position, achine parameters, and relationship between the flux linkage and rotor position, the rotor position can be estimated. At the very beginning of the integration the initial flux linkage has to be known precisely to estimate the next step flux linkages. This means that the rotor has to be at a known position at the start
*14, 16, and 20+. Last equation (20) written in – coordinates depends on the terminal voltage and the stator current. Using the – frame the equation for the rotor angle can be written as follows:
where L is the winding inductance.
The actual rotor angle using the dq frame can be calculated with:
Then the estimated is differentiated to get the speed of the rotor.

Simulation Results
We simulated the proposed model shown in figure 6 with the parameters shown in Table 1.
Figure 6: Simulink Model of the Simulated system.
Tablet 1: Parameters of the simulated Modal
Parameter Name
Value
DC Link Voltage
300 V
Rated Torque
33.9 Nm
Rated Speed
2200 RPM
Stator Inductance
0.00153 H
Stator Resistance
0.129 ohm
Flux by Magnet
0.1821 Wb
Figure 7: speed and torque of the motor it takes about 0.1 seconds to regulate the speed.
Figure 8: Measured current waveform by measurement vector insertion it shows that the waveforms are almost sine waves hence no distortion present.

Conclusion
The simulation result shows that the proposed Space vectored controlling with the measurement vectors insertion gives great controlling as well as the system need only one current sensor which makes it robust the other advantage of the proposed method that it can be applied to any type of PMSM without much modification.
References

Hongrae Kim & Thomas M. Jahns Phase Current Reconstruction for AC Motor Drives Using a DC Link Single Current Sensor and Measurement Voltage Vectors, IEEE TRANSACTIONS ON POWER ELECTRONICS, VOL. 21, NO. 5, SEPTEMBER 2006.

J. Rais & M. P. Donsion Permanent Magnet Synchronous Motors (PMSM). Parameters influence on the synchronization process of a PMSM, http://www.icrepq.com/icrepq 08/409rais.pdf.

Phi Hung Nguyen, Emmanuel Hoang and Mohamed Gabsi Power Loss Evaluation of a Surface Mounted Permanent Magnet
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K. Boughrara, D. Zarko, R. Ibtiouen & O. Touhami and A. Rezzoug Magnetic Field Analysis of Inset and SurfaceMounted PermanentMagnet Synchronous Motors Using SchwarzChristoffel Transformation, IEEE TRANSACTIONS ON MAGNETICS, VOL. 45, NO. 8, AUGUST 2009.

Milan Brejl & Michal Princ Permanent Magnet Synchronous Motor Vector Control, Driven by eTPU on MPC5500, Freescale Semiconductor Application Note, AN3206 Rev. 1, 03/2012.

Al Kassem Jebai, Francois Malrait, Philippe Martin and Pierre Rouchon Sensorless position estimation of PermanentMagnet Synchronous Motors using a saturation model, http://arxiv.org/abs/1207.5743, 2012.

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J. Rais 1 and M. P. Donsion Permanent Magnet Synchronous Motors (PMSM). Parameters influence on the synchronization process of a PMSM, http://www.icrepq.com/icrepq08/409rais.pdf.

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